Blog Archives

Approximating the cut distribution

October 1, 2017
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Approximating the cut distribution

Hi, This post is about computational issues with the cut distribution for Bayesian inference in misspecified models. Some motivation was given in a previous post about a recent paper on modular Bayesian inference. The cut distribution, or variants of it, might play an important role in combining statistical models, especially in settings where one wants to propagate uncertainty […]

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Unbiased Hamiltonian Monte Carlo with couplings

September 17, 2017
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Unbiased Hamiltonian Monte Carlo with couplings

With Jeremy Heng we have recently arXived a paper describing how to remove the burn-in bias of Hamiltonian Monte Carlo (HMC). This follows a recent work on unbiased MCMC estimators in general on which I blogged here. The case of HMC requires a specific yet very simple coupling. A direct consequence of this work is that Hamiltonian Monte […]

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Statistical learning in models made of modules

September 9, 2017
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Statistical learning in models made of modules

    Hi, With Lawrence Murray, Chris Holmes and Christian Robert, we have recently arXived a paper entitled “Better together? Statistical learning in models made of modules”. Christian blogged about it already. The context is the following: parameters of a first model appear as inputs in another model. The question is whether to consider a “joint model […]

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Sampling from a maximal coupling

September 5, 2017
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Sampling from a maximal coupling

  Hi, In a recent work on parallel computation for MCMC, and also in another one, and in fact also in an earlier one, my co-authors and I use a simple yet very powerful object that is standard in Probability but not so well-known in Statistics: the maximal coupling. Here I’ll describe what this is […]

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Update on inference with Wasserstein distances

August 15, 2017
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Update on inference with Wasserstein distances

Hi again, As described in an earlier post, Espen Bernton, Mathieu Gerber and Christian P. Robert and I are exploring Wasserstein distances for parameter inference in generative models. Generally, ABC and indirect inference are fun to play with, as they make the user think about useful distances between data sets (i.i.d. or not), which is sort of implicit in classical […]

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Unbiased MCMC with couplings

August 14, 2017
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Unbiased MCMC with couplings

    Hi, With John O’Leary and Yves Atchadé , we have just arXived our work on removing the bias of MCMC estimators. Here I’ll explain what this bias is about, and the benefits of removing it. What bias? An MCMC algorithm defines a Markov chain , with stationary distribution , so that time averages of the chain […]

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Particle methods in Statistics

June 30, 2017
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Particle methods in Statistics

Hi there, In this post, just in time for the summer, I propose a reading list for people interested in discovering the fascinating world of particle methods, aka sequential Monte Carlo methods, and their use in statistics. I also take the opportunity to advertise the SMC workshop in Uppsala (30 Aug – 1 Sept), which […]

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Likelihood calculation for the g-and-k distribution

June 10, 2017
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Likelihood calculation for the g-and-k distribution

    Hello, An example often used in the ABC literature is the g-and-k distribution (e.g. reference [1] below), which is defined through the inverse of its cumulative distribution function (cdf). It is easy to simulate from such distributions by drawing uniform variables and applying the inverse cdf to them. However, since there is no closed-form […]

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ABC in Banff

March 6, 2017
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ABC in Banff

Hi all, Last week I attended a wonderful meeting on Approximate Bayesian Computation in Banff, which gathered a nice crowd of ABC users and enthusiasts, including lots of people outside of computational stats, whom I wouldn’t have met otherwise. Christian blogged about it there. My talk on Inference with Wasserstein distances is available as a video here (joint […]

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Statistical inference with the Wasserstein distance

January 26, 2017
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Statistical inference with the Wasserstein distance

Hi! It’s been too long! In a recent arXiv entry, Espen Bernton, Mathieu Gerber and Christian P. Robert and I explore the use of the Wasserstein distance to perform parameter inference in generative models. A by-product is an ABC-type approach that bypasses the choice of summary statistics. Instead, one chooses a metric on the observation space. […]

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